Anzeige
Erschienen in:
2015 | OriginalPaper | Buchkapitel
Multiple Contextual Task Recognition for Sharing Autonomy to Assist Mobile Robot Teleoperation
verfasst von : Ming Gao, Thomas Schamm, J. Marius Zöllner
Erschienen in: Intelligent Robotics and Applications
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
To efficiently facilitate autonomy sharing for assisting mobile robot teleoperation, in this paper we propose a method to recognize four contextual task types executed by the human operator: doorway crossing, object inspection, wall following and robot docking, which extends our previous approach, where only the first two task types were considered. We employ a set of simple but highly distinctive task features to efficiently describe each task type, which is adopted by a Gaussian Mixture Regression (GMR) model combined with a recursive Bayesian filter (RBF) to infer the most probable task the human operator executes across multiple candidates during operation. We demonstrate the effectiveness of the approach with a variety of tests in a cluttered indoor scenario.